Local orbital-angular-momentum dependent surface states with topological protection

Chiral surface states along the zigzag edge of a valley photonic crystal in the honeycomb lattice are demonstrated. By decomposing the local fields into orbital angular momentum (OAM) modes, we find that the chiral surface states present OAM-dependent unidirectional propagation characteristics. Particularly, the propagation directivities of the surface states are quantified by the local OAM decomposition and are found to depend on the chiralities of both the source and surface states. These findings allow for the engineering control of the unidirectional propagation of electromagnetic energy without requiring an ancillary cladding layer. Furthermore, we examine the propagation of the chiral surface states against sharp bends. It turns out that although only certain states successfully pass through the bend, the unidirectional propagation is well maintained due to the topology of the structure.Comment: 9 pages, 6 figure

A brief review of topological photonics in one, two, and three dimensions

Topological photonics has attracted increasing attention in recent years due to the unique opportunities it provides to manipulate light in a robust way immune to disorder and defects. Up to now, diverse photonic platforms, rich physical mechanisms and fruitful device applications have been proposed for topological photonics, including one-way waveguide, topological lasing, topological nanocavity, Dirac and Weyl points, Fermi arcs, nodal lines, etc. In this review, we provide an introduction to the field of topological photonics through the lens of topological invariants and bulk-boundary correspondence in one, two, and three dimensions, which may not only offer a unified understanding about the underlying robustness of diverse and distinct topological phenomena of light, but could also inspire further developments by introducing new topological invariants and unconventional bulk-boundary correspondence to the research of topological photonics.Comment: 29 pages, 12 figures, 341 reference

First-principle calculation of Chern number in gyrotropic photonic crystals

As an important figure of merit for characterizing the quantized collective behaviors of the wavefunction, Chern number is the topological invariant of quantum Hall insulators. Chern number also identifies the topological properties of the photonic topological insulators (PTIs), thus it is of crucial importance in PTI design. In this paper, we develop a first principle computatioal method for the Chern number of 2D gyrotropic photonic crystals (PCs), starting from the Maxwell's equations. Firstly, we solve the Hermitian generalized eigenvalue equation reformulated from the Maxwell's equations by using the full-wave finite-difference frequency-domain (FDFD) method. Then the Chern number is obtained by calculating the integral of Berry curvature over the first Brillouin zone. Numerical examples of both transverse-electric (TE) and transverse-magnetic (TM) modes are demonstrated, where convergent Chern numbers can be obtained using rather coarse grids, thus validating the efficiency and accuracy of the proposed method.Comment: 13 pages, 6 figure

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Quantum information dynamics in a high-dimensional parity-time-symmetric system

Non-Hermitian systems with parity-time (PT) symmetry give rise to exceptional points (EPs) with exceptional properties that arise due to the coalescence of eigenvectors. Such systems have been extensively explored in the classical domain, where second- or higher-order EPs have been proposed or realized. In contrast, quantum information studies of PT-symmetric systems have been confined to systems with a two-dimensional Hilbert space. Here, by using a single-photon interferometry setup, we simulate the quantum dynamics of a four-dimensional PT-symmetric system across a fourth-order exceptional point. By tracking the coherent, nonunitary evolution of the density matrix of the system in PT-symmetry unbroken and broken regions, we observe the entropy dynamics for both the entire system, and the gain and loss subsystems. Our setup is scalable to the higher-dimensional PT-symmetric systems, and our results point towards the rich dynamics and critical properties

MBA: a literature mining system for extracting biomedical abbreviations

<p>Abstract</p> <p>Background</p> <p>The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.</p> <p>Results</p> <p>A literature mining system MBA was constructed to extract both acronym-type and non-acronym-type abbreviations. An abbreviation-tagged literature corpus, called Medstract gold standard corpus, was used to evaluate the system. MBA achieved a recall of 88% at the precision of 91% on the Medstract gold-standard EVALUATION Corpus.</p> <p>Conclusion</p> <p>We present a new literature mining system MBA for extracting biomedical abbreviations. Our evaluation demonstrates that the MBA system performs better than the others. It can identify the definition of not only acronym-type abbreviations including a little irregular acronym-type abbreviations (e.g., <CNS1, cyclophilin seven suppressor>), but also non-acronym-type abbreviations (e.g., <Fas, CD95>).</p

Programming moir\'e patterns in 2D materials by bending

Moir\'e superlattices in twisted two-dimensional materials have generated tremendous excitement as a platform for achieving quantum properties on demand. However, the moir\'e pattern is highly sensitive to the interlayer atomic registry, and current assembly techniques suffer from imprecise control of the average twist angle, spatial inhomogeneity in the local twist angle, and distortions due to random strain. Here, we demonstrate a new way to manipulate the moir\'e patterns in hetero- and homo-bilayers through in-plane bending of monolayer ribbons, using the tip of an atomic force microscope. This technique achieves continuous variation of twist angles with improved twist-angle homogeneity and reduced random strain, resulting in moir\'e patterns with highly tunable wavelength and ultra-low disorder. Our results pave the way for detailed studies of ultra-low disorder moir\'e systems and the realization of precise strain-engineered devices

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty

IL11 is elevated in systemic sclerosis and IL11-dependent ERK signalling underlies TGF beta-mediated activation of dermal fibroblasts

Objectives Interleukin 11 (IL11) is highly upregulated in skin and lung fibroblasts from patients with systemic sclerosis (SSc). Here we tested whether IL11 is mechanistically linked with activation of human dermal fibroblasts (HDFs) from patients with SSc or controls. Methods We measured serum IL11 levels in volunteers and patients with early diffuse SSc and manipulated IL11 signalling in HDFs using gain- and loss-of-function approaches that we combined with molecular and cellular phenotyping. Results In patients with SSc, serum IL11 levels are elevated as compared with healthy controls. All transforming growth factor beta (TGFβ) isoforms induced IL11 secretion from HDFs, which highly express IL11 receptor α-subunit and the glycoprotein 130 (gp130) co-receptor, suggestive of an autocrine loop of IL11 activity in HDFs. IL11 stimulated ERK activation in HDFs and resulted in HDF-to-myofibroblast transformation and extracellular matrix secretion. The pro-fibrotic action of IL11 in HDFs appeared unrelated to STAT3 activity, independent of TGFβ upregulation and was not associated with phosphorylation of SMAD2/3. Inhibition of IL11 signalling using either a neutralizing antibody against IL11 or siRNA against IL11RA reduced TGFβ-induced HDF proliferation, matrix production and cell migration, which was phenocopied by pharmacological inhibition of ERK. Conclusions These data reveal that autocrine IL11-dependent ERK activity alone or downstream of TGFβ stimulation promotes fibrosis phenotypes in dermal fibroblasts and suggest IL11 as a potential therapeutic target in SSc